In Q4.2 (currently in Beta) you can customize the names of statistics for selected tables (Edit | Table/Plot Options | Output Text) or for the entire project (Edit | Project Options | Output Text). For example, change:

• Average to NPS.

• Column n to Base.

• Average to 均值 (if you are Chinese).

Go to www.q-demo.com for a sneak peek at the new Q dashboard. The dashboard is online and reads directly from Q. It can be automatically updated with new waves of data and it is iPhone and iPad compatible. All the plots shown in this dashboard will be available in Q4.2 which will be up for Beta testing soon.

Please have a look at Tim Bock's article in the current edition of International Journal of Market Research, titled: Improving the display of correspondence analysis using moon plots. www.ijmr.com

Check out this new review of Q by eKonometrics:

Q: A new software for analyzing survey data

Pick One – Multi, Pick Any, Pick Any – Grid, Date, Number and Number – Multi questions can now be selected as splitting questions. The multiple response questions are split and each variable is analysed separately. The numeric variables are split into five approximately-equally-sized categories.

More sophisticated methods are now used in most statistical tests that are performed when data is weighted.  In particular, Taylor series linearization is used when computing the standard errors.   This results in a change in automatic statistical tests (i.e., the tests that determine whether cells on tables are highlighted as being significant or not).  The Q4.1 Reference Manual contains more information on this.  The key benefit of this change to the testing is that tests are more powerful when the weight is correlated with one or more of the variables used to construct the table (i.e., selected in the blue and brown drop-down menus).  There is an additional benefit for agencies that work with government clients: the testing will generally be more consistent with the way that testing is conducted by government statistical agencies.

There have been two additional changes to statistical testing.   It is now possible to specify a minimum sample size used in testing (in Edit | Project Options... | Statistical Assumptions).  And, corrections for multiple comparisons now ignore any cells in a table where no p-value could be computed (most commonly, this is for columns containing no data).

In addition to the standard automatic tests of significance, it is now possible to show comparisons between different columns on a table.  Select Statistics – Cells and Column Comparisons to see letters showing which columns are significantly different.  As with the standard tests, the False discovery rate is automatically applied.

Sample sizes can be placed on the bottom of columns or on the right of rows by selecting Statistics – Below and Column N or Statistics – Right and Row N.  The Row N and the Column N are now computed based on all the non-missing data in that row or column (previously, with multiple response questions which did not add up to 100%, the Row N and Column N reflected the total number of respondents with data).  This change has been requested by many users.

The reporting of Base n on Date questions has been modified to be consistent with the presentation with other styles of questions.  Previously, Base n referred to the total number of observations for the time period being displayed; now, it refers to the total number of observations in the data (Column n and Row n will reveal the number of observations in the time period).

Where a table involves a Date question, you can now use moving averages and local polynomial regression to smooth the data.  The Q4.1 Reference Manual contains a new section which describes how this is done and which also highlights other tools in Q for analysing tracking studies.

Q4.1 now supports 64-bit processors, which are becoming the norm when you purchase a new PC or laptop.  The advantages of 64-bit support for Q includes:

• No out of memory problems with big tables or big data sets, compared to a 32-bit limit of around a million cases.
• Q can use as much RAM as your computer has, compared to a 32-bit limit of around 2GB.

To take advantage of Q's 64-bit support, you must be running a 64-bit edition of Windows Vista or Windows 7.  Here is a guide from Microsoft to check whether you are already using a 64-bit edition of Windows.

The only disadvantage of 64-bit support is that the statistics for Experiment tables and the results of Segmentation analysis differ from what you see on a 32-bit edition of Q by around 1%.  These differences occur because 64-bit Q can perform calculations with more precision than 32-bit, meaning that Q effectively has access to more decimal places, and this can cause some differences with Experiments or Segmentation because of the many complex calculations involved in computing their results.

Another trend in computing is the uptake of dual-core and quad-core processors.  Recognising this trend, we have initially made Q's most expensive calculations support multiple cores: Experiment and Ranking statistics.  This means when you view an Experiment question in a table, or compute its Segments, Q can be up to twice as fast if you have a dual-core processor, or up to four times as fast if you have a quad-core processor.

Q5 will have even more areas targeted for multi-core support - please tell us if there is a particular area of Q that is slow for you and we will make it part of the plan for Q5.